Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs supplied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming. Management of a field using precision farming techniques requires a farmer to gather information or data relating to various characteristics or parameters of the field on a site-specific basis. Data may be obtained in a number of ways including taking manual measurements, remote sensing or by sensing during field operations. A farmer may take manual measurements by visually noting characteristics of a field (e.g., insect infestation) and recording the position as he traverses the field, or by taking soil samples, recording the position, and analyzing them in a laboratory. Remote sensing may include taking aerial photographs of a field, or generating spectral images of the field from airborne or space borne multi-spectral sensors.
Spatially variable characteristic data may also be acquired during field operations using appropriate sensors supported by a combine, tractor, or other agricultural vehicle. Spatially-variable data may relate to local conditions of the field, farming inputs supplied to the field, or crops harvested from the field. The data may represent soil properties (e.g., soil type, soil fertility, moisture content, compaction or pH), crop properties (e.g., height, moisture content, or yield), or farming inputs supplied to the field (e.g., fertilizers, herbicides, water, insecticides, seeds, cultural practices or tillage techniques used). Other site-specific data may represent insect or weed infestation, land marks, or topography (e.g., altitude).
Once spatially-variable farming data are obtained, it would be desirable to classify the site-specific data at each position in a field into a finite, and preferably convenient, number of discrete management zones. For example, it may be desirable to take measurements of yield, fertilizer applied, and soil moisture at a plurality of positions throughout the field. It would then be desirable to classify the measurements into a limited number of management zones while noting the position or boundaries of these zones. Each zone would represent a region or regions in the field in which the site-specific data is most closely related with other site-specific data in that field based on some definable measure. By this analysis, a management zone map of the field or fields would be created. A management zone map may be used to make general judgments about the field, or to generate prescription maps based upon the analysis results. Such a prescription map may then be used to generate command signals for variable rate controllers adapted to apply farming inputs to the field in amounts that vary as a function of the management zone at the positions in the field. Variable-rate controllers may be mounted on tractors, spreaders, or planters equipped with variable-rate applicators, and may be used to control the application rates for seeds, fertilizers, herbicides, and other farming inputs. By optimizing application rates based upon management zone information, the amounts of various farming inputs applied to the field can be optimized.
Classifying site-specific farming data, however, can be a complex task involving categorizing possibly a large number of measurements, each measurement having a continuous spectrum of possible values, at each position in the field, thereby leading to an infinite number of possible combinations of measurements at any discrete position. Therefore, it is desirable to have an automated system for classifying site-specific farming data, at each position in the field, into a finite number of management zones. Each position classified as belonging within a single management zone will have similar characteristics to every other position classified as belonging within that management zone.
Current systems do not provide easy to use tools for classifying site-specific farming data into a discrete number of management zones. The inability to organize site-specific farming data in such a manner may prevent the discovery of relationships and interactions between different characteristics that occur within fields. The inability to discover relationships may result in farming inputs being applied in a less than optimal manner, resulting in reduced crop yield or excess environmental damage. The inability to quantitatively organize the effects of farming inputs on yield may prevent the performance of an informed cost-benefit analysis to determine an optimum approach to take. Additionally, the inability to classify site-specific farming data in management zones may discourage a farmer from "experimenting" by applying certain farming inputs in determining the effect on field characteristics since the farmer may not be able to fully analyze the results. These problems are not solved by existing systems since they do not provide easy to use tools for classifying site-specific farming data. Further, existing systems may also include data tables or may perform calculations which do not accurately reflect commonalties that exist between characteristics of a particular field being farmed.